Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approx...
In this paper we propose a method for on-line max auditing of dynamic statistical databases. The method extends the Bayesian approach presented in [2], [3] and [4] for static data...
Originally devoted to specific applications such as biology, medicine and demography, duration models are now widely used in economy, finance or reliability. Recent works in var...
Roland Donat, Philippe Leray, Laurent Bouillaut, P...
We describe an approach to building brain-computer interfaces (BCI) based on graphical models for probabilistic inference and learning. We show how a dynamic Bayesian network (DBN...
Abstract— Real-world robotic environments are highly structured. The scalability of planning and reasoning methods to cope with complex problems in such environments crucially de...